Basic Microcomputing and Biostatistics

1. Introduction to the Computer. - Computer Memory. - Data Processing. - Control. - Kinds of Computers. - Batch Processing and Time Sharing. - When Not to Use the Computer. - Problems. - 2. Understanding Experimental Error and Averages. - Significant Figures. - Sources of Errors. - Truncation Errors. - Propagation of Errors. - Euler's Theorem and Variations. - Geometrical Interpretation of Euler's Theorem. - Measures of Central Tendency. - The Means of Means. - Problems. - 3. Understanding Experimental Data Dispersion. - The Average Deviation. - The Mean Deviation Independent of the Sign. - Population Parameters and Sample Statistics. - The Standard Deviation. - Relative Standard Deviation. - Grouped Data and Frequency Distributions. - Cumulative Frequency Distributions. - Application: Dose-Response Curve. - Frequency Grouping and Pictorial Representation of Continuous Data. - Quartiles and Percentiles. - Skewness and Kurtosis. - Problems. - 4. Understanding Probability. - Simultaneous and Equally Acceptable Probabilities. - Application: Heredity. - The Urn Problem: Dependent Probabilities. - Conditional Probabilities. - Bayes' Theorem. - Application of Bayes' Theorem: Diagnosis. - Permutations. - Combinations. - The Binomial Coefficient. - Problems. - 5. Determining Probability Distributions. - Tree Diagrams. - Application: Heredity. - Modal and Mean Scores. - The Binomial Distribution. - Effect of p on the Binomial Distribution. - Mean Occurrences for Data Groups of N. - Application: Evaluation of a New Treatment. - Problems. - 6. Using the Poisson Distribution. - Application: Disintegration of Radioactive Nuclei. - Mean Variance and Standard Deviation for the Poisson Distribution. - Computer Graphics. - Problems. - 7. Using the Normal Distribution. - Relative Areas by Computer Graphics. - Normalization of theGaussian Distribution. - The Mean Variance and Standard Deviation of the Gaussian Distribution. - The Standard Normal Deviate z. - Computer Integration. - Tabulated Areas Under the Normal Curve. - The Most Common Areas and Limits on z. - One- and Two-Tailed Tests. - Problems. - 8. Determining Probabilities: How to Apply the Chi Square Test and the Student's t-Test. - Chi Square Detection of a Biased Coin. - Degrees of Freedom. - The Central Limit Theorem. - Situations in Which the Population Mean Is Known But Not Its Standard Deviation. - Determination of Confidence Limits When No Population Parameters Are Known. - Confidence Limits. - The Difference Between Means. - Problems. - 9. Finding Linear Functions: The Principle of Least Squares. - Linear Functions. - Least Squares Treatment of Linear Functions Passing Through the Origin. - The Correlation Coefficient. - Applications. - Least Squares with Two Minimization Parameters. - Simultaneous Equations. - Solution of Simultaneous Equations by Cramer's Rule. - The Determinant of the Coefficients. - Cramer's Rule. - Solution of the Normal Equations for Linear Curve Fitting by Cramer's Rule. - Problems. - 10. Fitting Nonlinear Curves. - Boyle's Law. - Exponential Decay. - Second-Order Reaction Kinetics. - Enzyme-Catalyzed Reactions. - Unlimited Population Growth. - Limited Population Growth. - Nearly Linear Functions. - Problems. - 11. Solving Simultaneous Equations. - Simultaneous Equations in Matrix Form. - Matrix Algebra. - Simultaneous Equations by Matrix Inversion. - Simultaneous Analysis. - Multiple Regression. - Least Squares Treatment of Multiple Regression. - Problems. - Answers to Problems. Language: English